Methods for the analysis of population-genomic data

对整个基因组进行经济测序的方法的出现开创了种群基因组学领域。尽管全基因组测序具有以前所未有的精度估计种群遗传参数的潜力，但数据分析所必需的方法已经严重落后。拟议的工作将为人口基因组数据的分析制定一个一般的统计框架。一般的策略是在三个层面上推导和计算验证一组有效的群体遗传参数估计器：个体基因组；群体中的多个个体；以及多个群体。具体的次级项目包括测量核苷酸位置之间的变异和协变模式、种群细分水平，以及开发新的方法以促进基因组组装和遗传图谱的完善。基因组学研究界将非常重视开发高效的估计算法，这些方法将广泛应用于应用研究和基础研究。将特别注意确定最佳抽样策略，包括每个个体的序列覆盖深度和个体数量之间的权衡，以及阅读长度、数量和质量之间的权衡。因此，所产生的方法应该使研究人员能够从其现有的数据集中获取尽可能多的信息，同时也促进未来的设计战略，以最大限度地提高单位采样和测序工作的信息产量。将开发的软件将在国家基因组分析支持中心永久存放并免费提供，并将举办讲习班，协助用户社区实施这些工具。植物基因组研究项目正在共同资助这项研究。

The advent of methods for economically sequencing entire genomes has ushered in the field of population genomics. Although whole-genome sequencing harbors the potential to yield estimates of population-genetic parameters with unprecedented accuracy, the methods essential to the analysis of data have lagged behind greatly. The proposed work will develop a general statistical framework for the analysis of population-genomic data. The general strategy is to derive and computationally validate a set of efficient estimators for population-genetic parameters at three levels: individual genomes; multiple individuals within populations; and multiple populations. Specific subprojects include the measurement of patterns of variation and covariation among nucleotide sites, levels of population subdivision, and the development of novel methods to facilitate genome assembly and the refinement of genetic maps. Considerable emphasis will be focused on the development of efficient estimation algorithms for use by the genomics research community.These methods will be widely used in both applied and basic research. Special attention will be devoted to identifying optimal sampling strategies, including the tradeoffs between depth of sequence coverage per individual and numbers of individuals, and between length, number, and quality of reads. As a consequence, the resultant methods should enable investigators to harvest the maximum possible information from their existing data sets, while also promoting the future design strategies to maximize informational yield per unit sampling and sequencing effort. The software to be developed will be permanently housed and freely available at the National Center for Genome Analysis Support, and workshops will be held to assist the user community in the implementation of such tools. The Plant Genome Research Project is co-funding this research.